AF: Medium: Collaborative Research: Estimation, Learning, and Memory: The Quest for Statistically Optimal Algorithms

这个项目的目标是开发新的、高效的算法，从给定的数据量中提取尽可能多的信息。特别是，这项研究旨在发展对如何利用自然语言环境、医学和基因组环境以及基于网络或图形的环境中存在的结构的理解。许多基本类型的结构在差异很大的科学和技术环境中反复遇到；我们的目标是在最近专注于最简单的非结构化环境的工作的基础上，开发适用于这些不同环境的广泛适用的工具和见解。这个项目的一个中心组成部分是理论社区、机器学习社区和更广泛的以数据为中心的研究人员和实践者之间的密切互动和思想、问题和技术的转移。从技术角度来看，本研究侧重于三种基本类型的结构：几何结构、代数或低级结构，以及顺序数据中存在的结构(如自然语言)。对于前两种类型的结构，研究的重点是了解稀疏数据机制中的可能性和限制，其中数据量与数据的维度相当或次线性。在第三个背景下，重点是了解记忆在学习和预测任务中的作用。除了该项目的直接研究目标外，PI还广泛参与教学和推广，包括设计威斯康星大学S的新数据科学课程，以及在斯坦福大学开发关于算法和数据科学基础方面的新课程。

The goal of this project is to develop new, efficient algorithms that extract as much information as is possible from a given quantity of data. In particular, this research aims to develop an understanding of how to leverage structure that is present in natural language settings, medical and genomic settings, and network- or graph-based settings. Many fundamental types of structure are encountered repeatedly in widely varying scientific and technological settings; our goal is to build on a recent body of work that focused on the simplest unstructured settings, and develop broadly applicable tools and insights to these diverse settings. A central component of this project is a close interaction and transfer of ideas, problems, and techniques, between the theory community, the machine learning community, and the broader set of data-centric researchers and practitioners.From a technical perspective, this research focuses on three fundamental types of structure: geometric structure, algebraic or low-rank structure, and the structure that is present in sequentialdata (such as natural language). For the first two types of structure, the research focus is on understanding the possibilities and limitations in the sparse data regime where the amount of data is comparable to, or sublinear in, the dimensionality of the data. In the third setting, the focus is on understanding the role of memory for learning and prediction tasks.Beyond the direct research goals of the project, the PIs are extensively involved in teaching and outreach, including designing UW?s new data sciences curriculum, and developing new courses on algorithms and foundational aspects of data sciences at Stanford.